Adversarial learning is a widely used fair representation learning technique that aims to remove the information related to sensitive attributes from hidden representations [1]}.
It typically uses a discriminator to infer sensitive attributes from the representations learned by a model, and propagates negative gradients of attribute prediction loss to the model to help generate sensitive attribute-agnostic representations [2]}.
However, in many real-world scenarios, the sensitive attribute labels of many samples can be missing.
For example, most users on the YouTube platform do not provide their gender information [3]}.
In addition, it is very expensive and even impractical to collect or manually annotate sufficient data with sensitive attribute labels [4]}.
The discriminator in adversarial learning cannot be well-tuned if samples with labeled attributes are scarce, and therefore the bias information related to sensitive attributes encoded in the deep representations cannot be effectively eliminated to achieve good fairness.
